Risky riders: A comparison of personality theories on motorcyclist riding behaviour

Transportation Research Part F 62 (2019) 33–44

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Risky riders: A comparison of personality theories on motorcyclist riding behaviour Dylan Antoniazzi, Rupert Klein ⇑ Department of Psychology, Lakehead University, Thunder Bay, Ontario, Canada Center for Research on Safe Driving, Lakehead University, Thunder Bay, Ontario, Canada

a r t i c l e

i n f o

Article history: Received 20 July 2018 Received in revised form 10 December 2018 Accepted 11 December 2018

Keywords: Motorcycles Individual differences Personality Big Five Reinforcement Sensitivity Theory Stunts Errors Speeding Safety

a b s t r a c t Objective: Few studies have investigated the association between broad personality traits and motorcycle rider behaviours. Typically, studies have focused on specific variables such as Sensation Seeking and Aggression. This study extends the literature by investigating the trait facets of the Big Five and the Reinforcement Sensitivity Theory (RST). Method: An internet-based questionnaire comprised of traditional (Sensation Seeking, Aggression) and novel (Big Five, Reinforcement Sensitivity Theory) personality scales, and the Motorcycle Rider Behaviour Questionnaire (MRBQ) were posted on various motorcycle internet forums. Results: A North American sample of 550 motorcyclists completed the survey. Four separate hierarchical regression analyses were conducted with each personality theory entered step-by-step to predict the four riding behaviours from the MRBQ (errors, speeding, stunts, protective gear use) as the criterion variables. Consistent with previous literature the traditionally used personality traits, Sensation Seeking, and Aggression, were strongly associated with riding errors, speeding, and especially performing stunts. The addition of the Big Five facets contributed negligibly to riding behaviours with the greatest explained variance accounting for errors. The addition of the Reinforcement Sensitivity Theory was especially useful in accounting for motorcycle riding errors, and the use of protective gear. Conclusion: Although research on personality theories and riding behaviour typically use more narrow trait scales this study demonstrates that physiologically-based, broader, measures such as the Reinforcement Sensitivity Theory have a strong association with riding behaviours. Future research would benefit from the inclusion of such measures. Crown Copyright Ó 2018 Published by Elsevier Ltd. All rights reserved.

1. Introduction Per mile traveled, fatalities occur 28 times more frequently for motorcyclists than occupants of four-wheel passenger vehicles (NHTSA, 2018). Personality traits have often been used to identify high-risk drivers of four-wheel passenger vehicles (Benfield, Szlemko, & Bell, 2007; Castellà & Pérez, 2004; Constantinou, Panayiotou, Konstantinou, Loutsiou-Ladd, & Kapardis, 2011; Harbeck & Glendon, 2013; Morton & White, 2013; Smith & Kirkham, 1981; Sullman, 2006), but few models of personality have been used to examine the behaviour of motorcyclists. Several factors set motorcyclists apart from other road users: lack of protection, increased physical and mental demands in operating the vehicle, poor visibility to other road users,

⇑ Corresponding author at: Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada. E-mail address: [email protected] (R. Klein). https://doi.org/10.1016/j.trf.2018.12.008 1369-8478/Crown Copyright Ó 2018 Published by Elsevier Ltd. All rights reserved.

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and having greater opportunities to engage in high-risk behaviours (e.g., speeding, stunts, and weaving through traffic). Considering these substantial differences, it is possible that the effects of personality on motorcycling may be unique compared to other vehicles (Broughton et al., 2009; Horswill & Helman, 2003; Shahar, Poulter, Clarke, & Crundall, 2010). To the authors’ knowledge the effect of personality on the operation of two-wheel vehicles (e.g., scooters and motorcycles), has not been examined among North American riders. The majority of research on two-wheel vehicles emanates from Southeast Asian, Africa, and Australia (Haque, Chin, & Lim, 2010; Ismail, Din, Lee, Ibrahim, & Sukimi, 2015; Ucho, Terwase, & Ucho, 2016; Watson, Tunnicliff, White, Schonfeld, & Wishart, 2007; Wong, Chung, & Huang, 2010) where contrasting climates, rural and urban infrastructures, motorcycle cultures/manufacturers, and laws enforcing rider behaviour and protective gear could differentially affect the observed relationships between personality and riding behaviour. The traits most frequently examined in these regions have been Sensation Seeking and trait-based Aggression (Haque et al., 2010; Watson et al., 2007; Wong et al., 2010). Building upon the prevailing studies that have investigated associations between personality and rider behaviour, this study attempts to extend our understanding by including common trait theories as well as physiologicallybased personality constructs in a North American population. 1.1. Sensation Seeking and Aggression Sensation Seeking is defined as a biologically rooted personality trait that is characterized by the pursuit of varied, novel, and extreme experiences (Zuckerman, 1994). Given their dispositional proclivity, Zuckerman (1994) speculated that individuals high in Sensation Seeking would gravitate towards high-risk activities such as motorcycling. Indeed, Australian researchers found that high sensation seekers, who ride motorcycles, are more likely to ride at extreme speeds, perform stunts, bend traffic rules, and push their own limits (Watson et al., 2007). Additionally, researchers from Singapore found that these individuals were more likely to ride within two hours of consuming alcohol, go above the speed limit with no fear of detection, and participate in illegal street races (Haque et al., 2010; Ismail et al., 2015). Among the studies to examine the relationship Sensation Seeking has with riding behaviour, investigators often include a measure of trait-based Aggression (Deffenbacher, Oetting, & Lynch, 1994). Similar to high Sensation Seekers, trait Aggression is associated with speeding, performing stunts and riding while impaired (Watson et al., 2007). Additionally, Aggression has been associated with more aggressive driving (Sullman, 2006) and more self-reported crashes within the last year (Haque et al., 2010). Sensation Seeking alone is not always related to an elevated risk of crash involvement. In a sample of Taiwanese riders (n = 683), Wong et al. (2010) found that Sensation Seekers self-reported being involved in fewer crashes; but if ever involved in a crash it was likely to be more severe. This protective effect was attributed to Sensation Seekers being more confident riders who are more attentive to surrounding traffic conditions. Among high-risk motorcyclists, such as Malaysian street racers, Sensation Seeking and Aggression was able to account for 16.1 percent of the variance in self-reported traffic violations. With previous studies focusing on Australian, and Asian populations the current study seeks to examine the relationship Sensation Seeking and Aggression have with North American motorcyclists, as well as determine if the addition of other popular trait based theories such the Big Five and Reinforcement Sensitivity Theory can prove useful as additional constructs for identifying high risk riders. 1.2. Big Five As one of the most widely used models in personality psychology, the Big Five has been praised as the most robust and replicated personality trait model to be developed (McCrae & Costa, 2008). In its conceptualization, Allport and Odbert (1936) utilized a lexical approach to analyzing dictionary entries for words that represent stable traits. From further factor analysis, Costa and McCrae (1980) discovered three, and later five factors that attempt to describe the underlying nature of personalities from a top-down descriptive approach. Today, the Big Five is comprised of a taxonomy of five dimensions which include Extraversion, Conscientiousness, Agreeableness, Neuroticism, and Openness to Experience. Extraversion, represents a tendency to be assertive, sociable, and energetic; Conscientiousness is associated with self-discipline, organization and problem solving (Pervin & John, 1999); Agreeableness with altruism, compassion and trustiness; Neuroticism with emotional instability, anxiousness, and rigidity (McCrae & John, 1992); and Openness to Experience with curiosity, creativity, and appreciation for aesthetics and values (McCrae & Costa, 2008). Despite the Big Five’s popularity, few studies have applied it to motorcyclists. One such study, conducted in Nigeria, studied the relationship between the Big Five personality traits and road safety rule compliance (e.g., legal requirements to wear safety equipment). Results revealed that the Big Five factors jointly accounted for 8 percent of the variance in rule compliance. When the trait factors were analyzed independently, only Agreeableness had a significant effect. The authors concluded that individuals scoring higher on Agreeableness are more likely to abide by societal rules, as well as be more cooperative and orderly while riding (Ucho et al., 2016). Another study among Australian moped riders identified four subtypes of riders based on a cluster analysis of personality characteristics (Brandau, Daghofer, Hofmann, & Spitzer, 2011). Subtypes were compared on how much they would relate to self-reported risk-taking, and injury severity. One subtype, characterized by having high Extraversion was shown to be the most likely to report being injured while riding. Consistent with previous studies to apply the Big Five to driving four-wheel vehicles, high Extraversion was associated with committing more traffic violations, increased involvement in fatal and non-fatal crashes, and being more likely to report performing risky

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and aggressive driving behaviours (Benfield et al., 2007; Smith & Kirkham, 1981). The present study aims to replicate and extend Ucho et al. (2016) and Brandau et al. (2011) findings within a North American sample, as well as compare the previously discussed personality models with the novel application of the Reinforcement Sensitivity Theory. 1.3. Reinforcement Sensitivity Theory Gray’s Theory of Reinforcement Sensitivity (RST) is often conceptualized as a physiological model of personality that can be divided into two systems that regulate appetitive (approach) and aversive (avoidance) motivation (Gray, 1981). These systems have, historically, been given several labels but have most frequently been referred to as the Behavioral Approach System (BAS) and the Behavioural Inhibition System (BIS; Carver & White, 1994). With modern advancements in neurophysiology, the emotional responses associated with the BIS, being fear and anxiety, were given separate distinctions (Blanchard, Griebel, & Blanchard, 2001). The updated model defines the fear response (‘‘get me out of here”) as the Fear- Fight-Flight System, (FFFS), and the anxiety response (‘‘watch out for danger”) as the BIS (McNaughton & Gray, 2000). McNaughton and Gray (2000) extended upon the BAS framework by subdividing it into four subtraits. Being Reward Interest (RI), which is characterized as the initial motivation to seek out positive and novel experiences; Goal drivepersistence (GDP), associated with the consistency in pursuing goals when immediate awards are deferred (e.g., longterm goal setting); Reward reactivity (RR), by the excitement of performing an activity well such as winning, and Impulsivity, by the rapid action, in an approach to capture an immediate reward such eating, drinking, or sex (Corr, 2008). Since the RST has yet to be applied to a motorcyclist population, the closest comparable research is with four-wheel passenger vehicles. While driving, individuals with higher sensitivity to reward (BAS) scores report committing more traffic violations (Castellà & Pérez, 2004). Individuals scoring higher in sensitivity to punishment (BIS) are more likely to perform driving mistakes but are also more likely to report driving within the law (Constantinou et al., 2011). High BIS scorers also perceive certain driving behaviours (e.g., talking on a cellphone, driving under the influence of alcohol, etc. . .) to be more risky and consequently perform less of them. There has, however, been some inconsistency with the BIS’ effects on driver safety with one study demonstrating positive associations with being less likely to wear a seat belt and more likely to drive after drinking (Voigt et al., 2009). In contrast, individual’s scoring high in BAS-Reward Responsiveness (Reward Reactivity) perceived little risk from such behaviours and were more likely to report performing them (Harbeck & Glendon, 2013). To date, one study has examined the FFFS facet on driving (Morton & White, 2013). The study found that individuals with higher FFFS scores demonstrated poorer hazard detection responses during a driving simulator stress induction procedure (e.g., pedestrian emerging across the road at an un-signalled crossing). The authors concluded that high FFFS scores impaired performance by increasing sensitivity towards receiving a negative evaluation. Compared to four-wheel vehicles, the effects of the RST’s motivational structures are expected to have different, or even amplified effects amongst motorcyclists given the elevated risks, and the increased physical and mental demands in operating a powered two-wheeled vehicle. 1.4. The present study The present study investigates the relationship between conceptually distinct personality theories (Trait-Aggression, Sensation Seeking, The Big Five, The Reinforcement Sensitivity Theory) on self-reported riding behaviour. By determining which personality traits best align with specific riding behaviours, it will be possible to identify the traits that are most predictive of safe and unsafe riding. Riding behaviours of specific interest will be based on the Motorcycle Rider Behaviour Questionnaire (MRBQ) developed by Elliott, Baughan, and Sexton (2007). This is also the first to utilize the MRBQ on a North American population. Furthermore, this study serves as an opportunity to determine if future motorcycle research should be based primarily on Sensation Seeking and Aggression, or if the addition of the Big Five (objective 1) and RST (objective 2) can offer unique insight into understanding, and identifying individual differences in rider behaviour. 2. Method 2.1. Participants The University Research Ethics Board first approved this study. Participants were then recruited from local motorcycle dealerships, online motorcycle forums (e.g., www.reddit.com/r/motorcycles/) and Amazon Mechanical Turk (M-Turk), which is an online integrated participant compensation system where participants can be recruited rapidly and inexpensively. Research on the quality of M-Turk data has demonstrated that selecting potential respondents with a high reputation (95% of tasks were completed and approved by previous M-Turk requesters) ensures high-quality data without having to rely on additional questionnaires checking for inattention (Peer, Vosgerau, & Acquisti, 2013). The current study only selected M-Turk respondents with approval ratings of 98% or greater. Eligible participants were either from the United States or Canada and had to be over the minimum riding age of 16 years old. Potential participants were first presented online with a cover page and then indicated consent on the subsequent informed consent page and then completed an online questionnaire battery, hosted on Survey Monkey (a commercial online data collection platform), which included demographic, personality, and motorcycle riding behaviour measures.

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2.2. Measures Sensations Seeking was assessed with The Brief Sensation Seeking Scale (BSSS; Hoyle, Stephenson, Palmgreen, Lorch, & Donohew, 2002) which is comprised of eight self-report statements (e.g., ‘‘I like to do frightening things”), to which participants indicated their agreement on a 5-point Likert-type scale. The BSSS scale has a Cronbach a of 0.74 and has been demonstrated to be a valid measure of Sensation Seeking among young and middle-age adults (Hoyle et al., 2002). The Brief Aggression Questionnaire (Webster et al., 2015) assessed trait Aggression with twelve statements (e.g., ‘‘Given enough provocation, I may hit another person”) answered on a Likert-type scale from 1 ‘‘extremely uncharacteristic of me” to 5 ‘‘extremely characteristic of me”. The BAQ has demonstrated sufficient reliability with a Cronbach a of 0.80, and convergent validly with other aggression questionnaires (Webster et al., 2015). To examine the Big Five, participants completed The Big Five Inventory (BFI; Pervin & John, 1999) which is comprised of 44 short phrase statements to which participates indicate their agreement on a 5-point Likert-type scale. The questionnaire measures Extraversion (e.g., ‘‘Is talkative”), Agreeableness (e.g., ‘‘Is helpful and unselfish with others”), Conscientiousness (e.g., ‘‘Does a thorough job”), Neuroticism (e.g., ‘‘Is depressed and blue”), and Openness to Experience (e.g., ‘‘Is original, comes up with new ideas”). Within North America, surveys receive alpha reliabilities averaging above 0.80, with demonstrated convergent and divergent validity with other Big Five measures (Arterberry, Martens, Cadigan, & Rohrer, 2014; Soto & John, 2009). The Reinforcement Sensitivity Theory was measured with the Reinforcement Sensitivity Theory-Personality Questionnaire (RST-PQ; Corr & Cooper, 2016). The questionnaire is comprised of 65 statements that are answered on a 4-point Likert-type scale based on how accurately the statement describes the participant. The questionnaire measures the BIS (e.g., ‘‘People are often telling me not to worry”), FFFS (e.g., ‘‘Looking down from a great height makes me freeze”) and BAS subtraits: Reward Interest (RI; e.g., ‘‘I regularly try new activities just to see if I enjoy them”), Goal-Drive-Persistence (GDP; e.g., ‘‘I will actively put plans in place to accomplish goals in my life”), Reward Reactivity (RR; e.g., ‘‘I get a special thrill when I am praised for something I’ve done well”), and Impulsivity (e.g., ‘‘If I see something I want, I act straight away”). The RST-PQ has demonstrated good internal consistencies for the FFFS (0.78), BIS (0.93), and BAS subtraits: RI (0.75), GDP (0.86), RR (0.78), and Impulsivity (0.74; Corr & Cooper, 2016). The RST-PQ has additionally demonstrated acceptable internal and convergent validity (Krupic´, Corr, Rucˇevic´, Krizˇanic´, & Gracˇanin, 2016). Motorcycle riding behaviour was assessed with the Motorcycle Rider Behaviour Questionnaire (MRBQ; Elliott et al., 2007). Questions are answered on a 6-point Likert-type format, and assess speeding (e.g., ‘‘Exceed the speed limit on a residential road”), stunts (e.g., ‘‘Attempt to do, or actually do, a wheelie”), errors (e.g.,‘‘Skid on a wet road or manhole cover”), and protective gear use (e.g., ‘‘Wear a protective jacket (leather or non-leather)?”). The factors of errors and speeding have demonstrated good internal consistencies over 0.80, whereas stunts and protective gear use have been found to have internal consistencies below 0.70 among novice riders (Sakashita et al., 2014). The MRBQ has also demonstrated predictive validity with self, and third-party-reported riding outcomes. Sakashita et al. (2014) observed that among Australian motorcyclists (n = 1305) that the MRBQ factors: errors and speeding were positively associated with self-reported crashes; stunts with police reported crashes; and protective gear inversely related with police reported offences. 2.3. Statistical analysis Four hierarchical regression models were performed to examine the effect of personality on speeding, stunts, errors and protective gear use. With years of riding experience being shown to have an inverse relationship with the likelihood of being in involved in a crash (Savolainen & Mannering, 2007), it was included in step one with sex and age as control variables. The order in which the remaining personality theories were entered was determined by prior evidence supporting a relationship with the predictors and riding behaviour. Well-established trait variables (Sensation Seeking and Aggression; Haque et al., 2010; Watson et al., 2007) were entered in the second step, and the Big Five traits (Brandau et al., 2011; Ucho et al., 2016) entered in step three, and since there was no research examining the relationship the RST has with riding behaviour it was entered into the fourth and final step. 3. Results 3.1. Sample characteristics Study solicitation was able to draw 1522 individuals to at least visit the study webpage. From this population, 332 (21%) participants were removed for not being from either the United States or Canada reducing the sample to n = 1190. Less than half of participants went on to complete the survey allowing for a final sample of n = 550. Based on previous studies assessing the response rates of online data collection, the current study was able to retain an exceptional percentage of participants (46%; Deutskens, de Ruyter, Wetzels, & Oosterveld, 2004; Sax, Gilmartin, & Bryant, 2003). Of the remaining participants 66.5% were not missing any data, 26.9% were missing less than 1% and the remaining 6.4% of participants were missing less than 6%. For missing items a series mean replacement was used for primary variables of interest.

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The majority of the final sample was recruited through online motorcycle forums (n = 469; 85.3%), followed by Amazon Mechanical Turk (n = 51; 9.3%), and local motorcycle dealerships (n = 30; 5.5%). The final sample consisted of 473 (86%) Americans, 77 (14%) Canadians, and was predominantly male n = 501 (91%), with n = 45 females (8.3%), and n = 4 participants who reported ‘‘other” (0.7%). Of the 550 participants 525 (95.6%) self-reported having a valid motorcycle licensed and 24 (4.4%) did not have a valid license. Table 1 displays additional demographic information and includes mean scores for primary predictor variables. See Table 2 for inter-correlations amongst the predictor variables. 3.2. Internal reliability The reported internal reliability of the MRBQ has been previously reported as ranging from acceptable to good (Sakashita et al., 2014). Similarly, in this study the Cronbach’s alpha for the entire scale was 0.80 with the subscales ranging from questionable to good: Errors (0.85), Stunts (0.72), Speeding (0.85) and Protective Gear (0.63). 3.3. Regression models 3.3.1. Riding Errors In the first regression (See Table 3), on motorcycle riding errors, step one’s inclusion of demographic factors (sex, age, years of riding) was statistically significant F(3,540) = 3.72, p < .05, and accounted for 2% of the adjusted variance in rider errors. Step two’s inclusion of the Sensation Seeking and Aggression accounted for an additional 7% of the adjusted variance, with the change being statistically significant F(2, 538) = 21.79, p < .001. Individuals scoring high on Sensation Seeking (p < .01), and Aggression (p < .001) were more likely to report performing errors while riding. To assess the first objective, step three added the Big Five traits. Similar to Ucho et al. (2016) the Big Five was statistically significant F(5, 533) = 5.00, p < .001, and contributed a 4% change to the explained variance, with all five traits having negative associations with rider errors (all ps < 0.05). To address the second objective, the RST traits (FFFS, BIS, BAS subtraits) were entered into step four, and accounted for a 9% change in the adjusted variance that was statistically significant F(3, 527) = 10.38, p < .001. Those with higher FFFS (p < .001), and BIS (p < .001) scores were more likely to report performing errors while riding. Results revealed that the Big Five and RST both accounted for statistically more variance in riding errors than demographic, and well-established trait variables alone. Moreover, the RST accounted for the most explained variance, with the BIS and FFFS traits producing statistically significant associations with errors. 3.3.2. Rider Speeding When predicting self-reported speeding, step one’s inclusion of demographic variables accounted for 6% of the adjusted variance, F(3,540) = 11.46, p < .001; with younger riders being more likely to report speeding on a motorcycle (p < .05). In Step 2, the addition of the established personality traits accounted for a 6% change in explained variance. Although when

Table 1 Descriptive statistics for independent variables (n = 550). Measure

M (SD)

Range

Rider Demographic Factorsy Age Years Actively Riding

33.36 (12.49) 9.03 (11)

17–74 <1–54

Personality Factors BSSS BAQ

13.5 (3.06) 3.2 (0.99)

4.5–20 1.25–6.58

0.76 0.81

Big Five Inventory Conscientiousness Agreeableness Neuroticism Openness to Experience Extraversion

3.69 3.67 2.49 3.72 2.96

(0.68) (0.65) (0.88) (0.53) (0.86)

1.33–5.00 1.56–5.00 1.00–5.00 1.60–5.00 1.00–5.00

0.77 0.77 0.86 0.63 0.86

RST-PQ FFFS BIS

1.81 (0.54) 2.26 (0.62)

1.00–4.00 1.05–3.86

0.76 0.87

BAS Subtraits Reward Reactivity Impulsivity Goal Drive Persistence Reward Interest

2.60 2.46 3.01 2.83

1.30–4.00 1.13–4.00 1.00–4.00 1.14–4.00

0.82 0.67 0.86 0.77

(0.53) (0.50) (0.61) (0.56)

Cronbach’s Alpha

Note: BSSS = Brief Sensation Seeking Scale; BAQ = Brief Aggression Questionnaire; BFI = Big Five Inventory; RST-PQ = Reinforcement Sensitivity Theory Personality Questionnaire; FFFS = Fear-Fight-Flight-System; BIS = Behavioural Inhibition System; BAS = Behavioural Approach System. y (n = 547).

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Table 2 Bivariate correlations between predictor variables. 1.

2.

3.

4.

1. 2. 3. 4.

Age Years Actively Riding BSSS BAQ

– 0.75** 0.26** 0.25**

5.

6.

7.

8.

9

– 0.19** 0.13*

– 0.26**

–

Big Five Inventory 5. Conscientiousness 6. Agreeableness 7. Neuroticism 8. Openness to Experience 9. Extraversion

0.25** 0.17** 0.22** 0.14** 0.16**

0.23** 0.11* 0.24** 0.06 0.18**

0.11* 0.08 0.06 0.29** 0.26**

RST-PQ 10. FFFS 11. BIS

0.16**  0.35**

0.14** 0.33**

BAS Subtraits 12. Reward Interest 13. Goal-Drive Persistence 14. Reward Reactivity 15. Impulsivity

0.03 0.01 0.23** 0.21**

0.06 0.03 0.18** 0.16**

10.

11.

0.14** 0.55** 0.35** 0.03 0.02

– 0.18** 0.38** 0.10* 0.15**

– 0.32 0.14** 0.20**

– 0.17** 0.31**

– 0.27**

–

0.24** 0.05

0.07 0.30**

0.09* 0.36**

0.02 0.24**

0.37** 0.79**

0.12* 0.06

0.15** 0.43**

– 0.40**

–

0.38** 0.09* 0.26** 0.50**

0.00 0.05 0.23** 0.31**

0.23** 0.44** 0.10* 0.23**

0.21** 0.14** 0.08 0.07

0.23** 0.23** 0.13* 0.17**

0.36** 0.25** 0.12* 0.18**

0.42** 0.27** 0.24** 0.25**

0.05 0.00 0.29** 0.16**

0.09* 0.11* 0.27** 0.27**

12.

13.

14.

15.

– 0.52** 0.40** 0.38**

– 0.29** 0.15**

– 0.69**

–

Note: BSSS = Brief Sensation Seeking Scale; BAQ = Brief Aggression Questionnaire; BFI = Big Five Inventory; RST-PQ = Reinforcement Sensitivity Theory Personality Questionnaire; FFFS = Fear-Fight-Flight-System; BIS = Behavioural Inhibition System; BAS = Behavioural Approach System. * p < .05. ** p  .001.

D. Antoniazzi, R. Klein / Transportation Research Part F 62 (2019) 33–44

Variable

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D. Antoniazzi, R. Klein / Transportation Research Part F 62 (2019) 33–44 Table 3 Hierarchical regression of personality traits on riding behaviours. Riding Behaviour Errors Variable Step 1 Age Sex Yr/Rd Step 2 BSSS Total BAQ Total Step 3: Big 5 Inventory Conscientiousness Agreeableness Neuroticism Openness to Experience Extraversion Step 4: RST-PQ Fear Fight Flight System Behavioural Inhibition System (BAS) Reward Reactivity (BAS) Impulsivity (BAS) Goal-Drive Persistence (BAS) Reward Interest Adjusted Total R2 n

DR2 0.02

Speeding b

*

DR2 0.06

***

0.02

0.09***

0.03

0.20*** 544

0.23*** 0.02 0.13* 0.01*

0.02*

***

0.02 0.12* 0.02*

0.13** 0.08 0.26*** 0.02 0.06 0.01*

0.18*** 0.10 0.08 0.11 0.04 0.07 0.15*** 544

0.04

0.17** 0.19***

0.00 0.15** 0.05 0.04 0.07

0.26*** 0.34*** 0.10 0.03 0.03 0.03

b

0.20** 0.05 0.16** 0.09***

**

DR 2 ***

0.08

0.11* 0.05

0.12** 0.11* 0.27*** 0.14** 0.13*

Protective Gear b

***

0.06*** 0.13** 0.19***

0.04***

DR2

0.13* 0.08 0.02

0.09 0.05 0.01 0.07***

Stunts b

0.06 0.05 0.07 0.08 0.22*** 0.06*** 0.06 0.12 0.14* 0.11 0.11* 0.19***

0.03 0.19** 0.05 0.09 0.03 0.01 0.18*** 544

0.10*** 535

Note: DR2 = Change in R2; Sex = coded as 0 = female, 1 = male; Yr/Rd = Years Actively Riding; BSSS = Brief Sensation Seeking Scale; BAQ = Brief Aggression Questionnaire; RST = Reinforcement Sensitivity Theory Personality Questionnaire; Behavioural Approach System. * p  .05. ** p < .01. *** p < .001.

reviewing the coefficients only Sensation Seeking was (positively) significant (p < .05). The addition of the Big Five traits resulted in a 2% change in accounted variance, and the change was significant F(5, 533) = 2.49, p = .05; however, only the variable, Agreeableness was significant (p < .01), with more Agreeable riders being less likely to report speeding. The fourth step’s inclusion of the RST traits accounted for an additional 4% of explained variance, F(3, 527) = 4.28, p < . 001. With greater FFFS scores being associated with speeding less often (p < .001). Results indicate that the Big Five, and the RST were able to account for additional variance, albeit not to a large extent, in speeding than the demographic and previously studied trait variables alone. 3.3.3. Rider stunts For the third regression with the criterion variable, stunts, step one was significant and accounted for 8% of the adjusted variance, F(3, 540) = 15.48, p < .001. Similar to speeding, older riders were less likely to perform stunts (p < .01), but those who reported riding longer performed stunts more frequently (p < .01) The addition of Sensation Seeking, and Aggression contributed a statistically significant change, F(2, 538) = 27.81, p < .001, and explained 9% in of the variance; with both variables demonstrating positive associations (p < .01). The addition of the Big Five also resulted in a statistically significant change F(5, 533) = 2.86, p < .05, and slightly increased the explained variance by 2%. Individuals scoring high on Conscientious (p < .01), and Neuroticism (p < .001) were less likely to report performing stunts. The addition of the RST traits allowed for an additional 1% in explained variance. Despite this change being statistically significant, F(3, 527) = 1.80, p < .05, only the BIS facet was positively associated with stunts (p < .01). Consistent with results reported for speeding, the Big Five and RST were able to account for significantly more variance in stunts than the traditionally studied variables. 3.3.4. Protective gear Step one in the fourth regression, predicting protective gear use, was significant F(3, 532) = 6.55, p < .001 and accounted for 4% of the adjusted variance. Older individuals were more likely to report wearing protective gear, but those who had reported riding longer were less likely to do so (p < .05). The addition of Step two resulted in a significant change, F(2, 530) = 3.73, p < .05; however it only explained an additional 1% of the variance, with more Aggressive riders being less likely to wear protective gear (p < .05) The inclusion of the Big Five traits in the third step was significant F(5, 525) = 2.27, p = .05, and accounted, similarly, for a small increase in the explained variance (2%). Highly Extraverted riders reported wearing protective gear less frequently (p < .001). The inclusion of the RST traits added the highest amount of explained variance in the model (6%) and with a statistically significant change F(6, 519) = 5.43, p < .001. Protective gear was the only behaviour to

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draw significant associations with the BAS subtraits. Riders who scored high on Reward Reactivity (p < .05), Goal-Drive Persistence (p < .05), and Reward Interest (p < .001) all reported wearing protective gear more frequently. 4. Discussion The general purpose of this study was to replicate and extend the literature on individual trait differences and motorcycle riding behaviour. This was the first use of the MRBQ scale in a North American sample, and the results were similar to those reported elsewhere. The only notable difference was the null finding in relation to sex. Among Taiwanese motorcyclists, it has been found that males are statistically more likely to report speeding, and violating road rules (e.g., disregarding a red light on an empty road) than females (Wong et al., 2010). Our null-findings with sex may be attributable to South-Eastern Asian populations being more likely to utilize motorcycles as a form of year round transportation rather than recreation. Whereas North American female motorcyclists may represent a subgroup of females who have a greater interest in riding motorcycles for leisure, rather than necessity. In regards to traditionally used personality scales, similar results were reported for Sensation Seeking and Aggression as in other papers. Watson et al. (2007) found that Aggression and Sensation Seeking alone, accounted for 15% of the variance in speeding and stunt behaviour, and 8–9% of the variance in errors. The current study demonstrated weaker but similar findings by having both variables account for 6–9% of the variance in rider behaviour. This discrepancy may be, in part, due to Watson et al. (2007) conflating stunts and speeding into a unitary measure, rather than distinct behaviours. Furthermore, this study extends our understanding of the utility of several distinct personality models, by comparing how they relate to an array or risk-related behaviours. The first objective was to determine if the Big Five traits could predict riding behaviour above and beyond that accounted for by demographic and well-established trait variables (Sensation Seeking and Aggression). It was found that the Big Five does, albeit incrementally, add some explained variance to all models beyond the traditionally used questionnaires. Ucho et al. (2016) found that the Big Five accounted for 8% of the variance in road safety rule compliance (e.g., wearing safety gear). In the current study, the results demonstrated that the Big Five only accounted for 2–4% of the variance in riding behaviours, when already controlling for Sensation Seeking, Aggression and demographic variables. The current study extends Ucho et al. (2016) by adopting the MRBQ, a more comprehensive measure of rider behavior. Utilizing the four facets of the MRBQ is beneficial to researchers as it allows analyses beyond just rule compliance and differentiates between behaviours that are mistakes (errors) from those that are purposeful (speeding, stunts, and protective gear use). In addition to contributing unique variance in all models, several of the Big Five traits displayed unique relationships with each riding behaviour. Specifically, trait Conscientiousness had an inverse relationship with riding errors and stunts; riding errors have been associated with increased overall crash risk (Elliott et al., 2007). Therefore, these results are consistent with Brandau et al. (2011) findings that moped riders scoring high on Conscientiousness were the least likely to be injured while riding. The negative relationship Conscientiousness has with stunts is also consistent with previous research on personality, and safety related risk taking (Nicholson, Soane, Fenton-O’Creevy, & Willman, 2005). These findings suggest that Conscientious riders may pay greater attention to vehicle and environment-related cues, obey more traffic rules, and be overall less risky riders. Trait Agreeableness demonstrated an inverse relationship with errors and speeding; a finding that was partially consistent with that of Ucho et al. (2016) who found that riders scoring high in Agreeableness were the most likely to comply with road safety rules. Conversely, riders scoring low on Agreeableness may be less likely to comply with these rules and consequently put them at a greater risk for crash involvement. Our finding support Ucho et al. (2016) remarks that Agreeable riders are more likely to comply with societal rules, and be more ‘‘orderly” while riding. Trait Neuroticism had a significant, inverse, relationship with both stunts and errors. The inverse relationship with errors may be, in part, due to Neurotic individuals’ avoidance of ambiguous stimuli (Lommen, Engelhard, & van den Hout, 2010). Riders scoring higher on Neuroticism may minimize their exposure to riding situations or environments where they are unfamiliar or uncomfortable (e.g., riding on busy highways, low visibility conditions). Whereas less Neurotic riders may be more likely to perform a riding error by being more comfortable in situations or environments they are unfamiliar with. Neuroticism was also shown to have the strongest relationship with stunts than all other predictor variables, with highly Neurotic individuals being the least likely to report performing them. Given the negative relationship Neuroticism has with overall risk taking propensity, these results are consistent with the current literature (Nicholson et al., 2005). Furthermore, Neurotic individuals may perceive the risks associated with stunts to outweigh the rewards, or not feel confident enough to perform them. Openness to Experience’s inverse relationship with errors may be explained by the unique relationship this trait shares with stress. Previous research on Openness to Experience and stress regulation has shown that people who rank high in Openness are less vulnerable to stress’ aversive effects (e.g., high blood pressure, poor sleep quality; Williams, Rau, Cribbet, & Gunn, 2009). This buffering effect is the result of being more adaptive and flexible under stressful situations (Lee-Baggley, Preece, & DeLongis, 2005). Since life stress (e.g., work and family) (Rowden, Matthews, Watson, & Biggs, 2011) and driver stress (Matthews et al., 1998) have positive relationships with driver errors it is possible that individuals scoring high on Openness are less affected by these stressors and are overall less likely to make a mistake while riding. Extraversion demonstrated a positive association with errors and had the strongest overall, and negative association with protective gear use. The negative relationship observed with errors may be attributable to Extraverted motorcyclists engaging in behaviours that can distract them while riding thereby leading them to commit mistakes (e.g., talking to a passenger

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on the bike, or into a cellphone/headset). The negative association between Extraverts and protective gear use is somewhat surprising, and without speculating on the motivation of high scoring Extraverts we suggest future research investigate this association. The second objective evaluated the effects of the RST on riding behaviour. Compared to the traditionally studied personality traits (Sensation Seeking, Aggression) the RST contributed additional explained variance to all of the rider behaviours. Although the association with speeding and stunts contributed very little to the explained variance, the association with rider errors and protective gear use contributed the highest change in R-square with some of the strongest associations observed in this study. The FFFS facet of the RST is the system associated with an avoidant response to perceived or actual threats. Similar to previous research (Morton & White, 2013), the present study found a positive relationship between high FFFS scorers with riding errors. The inverse association the FFFS had with speeding was the strongest association observed for that behaviour, although it accounted for only 3% of the overall variance. Intuitively, individuals who are more fearful and readily avoid situations that are uncomfortable may find the arousal associated with speeding on a motorcycle to be more terrifying than exciting. This BIS facet displayed, amongst all of the personality traits, the strongest relationship with errors. These findings are consistent with Constantinou et al. (2011) who found that high BIS scores were associated with more driving mistakes amongst operators of four-wheel passenger vehicles. This association is best understood by the strong association the BIS has with anxiety (Carver & White, 1994; Corr, 2008). Research on trait anxiety and attention have demonstrated anxiety’s aversive effects on concentration, making anxious individuals more prone to distraction (Bishop, 2009). In combination with the current findings it may be suggested that high BIS scores hamper performance on perceptually and physically demanding tasks such as having to operate a motorcycle, and consequently increase one’s likelihood of performing errors. The BIS’ positive relationship with stunts is, however, less clear but partially consistent with previous findings. In a study examining the relationship between the BIS/BAS scales and risky health behaviours (e.g., alcohol misuse and safety practices like wearing a seatbelt), it was anticipated that the BIS would have an overall protective effect on health. Conversely, it was found that high BIS scores were associated with being less likely to wear a seat belt or bike helmet, and more likely to drive after drinking (Voigt et al., 2009). When taken into consideration with current findings, it may be suggested that predicting aversive health behaviours, high BIS scores may be considered more harmful. The addition of the strong BIS results is a unique contribution to this literature and merits, in our opinion, further study. However, as is consistent with the literature, Sensation Seeking and Aggression accounted for the greatest amount of variance for stunts. Likely, this reflects the utility of applying a more narrow trait description with a more focused and specific behaviour. The BAS subtraits Reward Reactivity (RR), Goal-Drive Persistence (GDP) and Reward Interest (RI) were the only facets of the RST to be significantly associated with any behaviour- being protective gear use. Reward Reactivity is characterized as the experience of pleasure when things are going well (Corr, 2008). The authors of the RST have noted RR to be the most important facet of the BAS (Corr & Cooper, 2016). Reward Interest is distinguished from RR insofar that RR is dependent on actual rewards needing to be present, whereas RI is more anticipatory. For example, individuals scoring high on RI are more likely to seek out opportunities that have the potential to be rewarding, rather than guaranteed. Individuals scoring high on RR and RI may derive much pleasure from riding a motorcycle and invest in the activity through buying clothing, and protective gear designed specifically for motorcyclists (e.g., motorcycle boots, body armor, gloves, etc. . .). Future research should determine if the construct of protective gear assesses a motivation to be safe or perhaps as a socially rewarding indication of one’s affiliation with a fashion or lifestyle brand. 4.1. Limitations and future directions Data collection from online motorcycle forums is potentially a limitation as the sample might not be representative of all North American riders. However, prior research examining the quality of online data collection has shown online samples to be similarly representative when compared to more traditional methods (e.g., paper-and-pencil) and has, in fact, been shown to be more diverse than convenience samples such as university students (Buhrmester, Kwang, & Gosling, 2011; Gosling, Vazire, Srivastava, & John, 2004). The use of self-reports over more objective measures (e.g., police reports) has been a point of controversy in the driving literature. A recent meta-analysis on the predictive validity of the Driver Behaviour Questionnaire (DBQ) reported several sources of bias (e.g., testing for differences in published versus unpublished data; comparing different source versus same source data; controlling for differences in exposure) in addition to finding that correlations between the DBQ and actual crash involvement to be close to zero (r < 0.07; Wåhlberg, Barraclough, & Freeman, 2015). This contentious issue seems to be isolated to driving as previously cited, Sakashita et al. (2014) found significant associations with MRBQ factors, Errors, and Speeding with self-reported crashes; as well as Stunts and Protective Gear respectively being associated with policereported crashes, and offences. Although, it is clear that self-reports are unlikely to surpass the validity of more objective measures of riding/driving behaviour, the methods employed in our study are consistent with others examining motorcycles and personality (Watson et al., 2007; Wong et al., 2010). Given the ambitious scope of the study: evaluating the riding behaviour of motorcyclists across North America, the use of more objective measures of riding behaviour while accounting for individual differences in personality would be much less feasible. Future research should look to further evaluate the

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psychometric properties of the MRBQ with multiple measures of exposure, behaviour, and riding outcomes across a diverse range of populations. This study observed no significant sex differences across all riding behaviours. These findings could be attributed to females only accounting for 8.3% of our sample and, as previously mentioned, may represent a distinct subgroup of the female population that has a particular interest in motorcycles. Although it is well known that motorcycle owners are over represented by males, female motorcycle ownership in the United States has grown more than two-fold, going up from 6% in 1990, to 14% in 2014 (Motorcycle Industry Council, 2017). With the established literature on risk-taking demonstrating reliable differences between males and females (Cross, Cyrenne, & Brown, 2013), future research on motorcyclist behaviour would benefit from a greater representation of female riders. Other factors, such as motivation for riding, frequency of riding and the type of motorcycl, have been known to produce variation in riding outcomes. For example, Harrison and Christie (2005) observed that among n = 794 motorcyclists in Australia, that those who road for pleasure or recreation on the weekends were at greater risk of crash involvement (per 100,000 km traveled) than those who rode daily for commuting or work. It has also been observed that different types of motorcycles are involved in fatal crashes at greater rates than others. This variability can be, in part, attributed to the power-to-weight ratio of the motorcycle (Haworth, & Blackman, 2013). Motorcycles with high power-to-weight ratios such as supersports have smaller frames, lighter parts, and mid to large size engines, allowing for greater acceleration and higher top speeds. Whereas a motorcycle such as a cruiser with a similar sized engine, but a heavier frame and body, will have a lower power-to-weight ratio. Such differences in vehicle properties have been reflected in the epidemiological literature. In an analysis of n = 27,524 motorcycle deaths in the United States, it was found that those on supersports had a fatal crash rate four times greater than those on cruisers (Teoh & Campbell, 2010). Although variation in rider motivation/behaviour, and motorcycle type was not controlled for in this study future research should look to control factors related to riding frequency, and motorcycle type to further inform our understanding of crash risk, and how personality can influence this relationship. Understanding the association that personality has with motorcycle riding behaviour has both theoretical and applied implications. Theoretically, it can help us determine the efficacy, and validity of different personality theories. In this study, the novel application of the Reinforcement Sensitivity Theory (RST) was able to contribute the most explained variance to half of the studied riding behaviours. Such findings warrant the further use of the RST for a wider range of risk-taking, and health-related behaviours. As well, this studied has applied implications as it can inform initiatives to reduce fatal motorcycle crashes. Personality research of this nature has been used in health communication programs to prevent injury, and promote healthy behaviours by framing safety messages to resonate with high-risk populations (Sherman, Mann, & Updegraff, 2006). 5. Conclusion Overall, this study replicated and extended the research associating personality traits to motorcycle riding behaviour. The addition of the Big Five and RST were able to account for a significant amount of variance for all riding behaviours when controlling for well-established traits. 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